FIG. 1 is a diagram showing an example of a fault analysis apparatus disclosed in Japanese Patent No. 3581934.
As shown in FIG. 1, fault analysis apparatus 100 includes abnormal-call amount monitoring section 101 such as an operation measurement, recording (OM) and transfer member or a fault recording and transfer member, threshold determination section 115, and determination result display section 116.
Fault analysis apparatus 100 configured as described above operates as follows.
Abnormal-call amount monitoring section 101 monitors monitoring target devices 131 and 132 to determine whether or not any log indicating occurrence of abnormality has been transmitted by monitoring target device 131 or 132. If there is such a log, abnormal-call amount monitoring section 101 counts a call amount that is a traffic amount per certain time depending on the type of abnormality. When the call amount within a given time is equal to or larger than a predetermined threshold, threshold determination section 115 notifies a maintenance operator of the abnormality as a fault through determination result display section 116.
Owing to such an operation, fault analysis apparatus 100 can automatically detect faults.
FIG. 2 is a diagram showing another example of a fault analysis apparatus disclosed in the document “JING WU, JIAN-GUO ZHOU, PU-LIUYAN, MING WU, “A STUDY ON NET WORK FAULT KNOWLEDGE ACQUISITION BASED ON SUPPORTVECTOR MACHINE”, Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, 18-21 Aug. 2005”.
As shown in FIG. 2, fault analysis apparatus 200 includes abnormality level monitoring section 201, abnormality level storage section 210, fault case registration section 211, case storage section 212, pattern learning section 213, knowledge storage section 214, pattern determination section 215, determination result display section 216, and determination correction input section 217 in order to manage monitoring target system 230 including monitoring target devices 231 to 234.
Fault analysis apparatus 200 configured as described above collects an abnormality level that is an index for the possibility of a failure in the apparatus or a line based on the results of monitoring of monitoring target devices 231 to 234.
FIG. 3 is a diagram showing the values of abnormality levels used for fault analysis apparatus 200 shown in FIG. 2.
The abnormality levels used for fault analysis apparatus 200 shown in FIG. 2 include values indicating whether or not a relevant link is inactive, an error rate, a congestion rate, a rejection rate, and a utilization rate as shown in FIG. 3.
Based on a combination of the abnormality levels obtained, pattern determination section 215 uses knowledge information stored in knowledge storage section 214 to determine whether or not a fault has occurred in monitoring target system 230. Pattern determination section 215 then presents the result of the determination to a maintenance operator through determination result display section 216.
The knowledge information stored in knowledge storage section 214 is generated in accordance with the following procedure.
First, the maintenance operator uses fault case registration section 211 to register past fault cases in case storage section 212.
Pattern learning section 213 generates knowledge information from fault cases stored in case storage section 212 and combinations of abnormality levels stored in abnormality level storage section 210 and stores the knowledge information in knowledge storage section 214. Here, the fault case is information indicating the time of occurrence of a fault and the type of the fault. Pattern learning means 213 generates knowledge information by pattern learning carried out using a pattern identification device called Support Vector Machine (SVM).
The SVM is described in “Hideki Aso, Hiroharu Tsuda, and Noboru Murata “Statistics for Pattern Recognition and Learning”, Iwanami Shoten, Publishers, pp. 107-123, 2005” in detail. In general, in pattern learning, first, a one-dimensional class (pattern) is estimated from a multidimensional variable. A variable used as the multidimensional variable is called a feature. Furthermore, a d-dimensional space formed by (d) features is called feature space Rd. Additionally, when an input variable is feature variable (x) (εRd) in the feature space and an output variable is a class (y) (ε{1,−1}), (y) changes when (x) passes a certain area in the feature space. A boundary at which the change occurs is called a hyperplane.
The hyperplane can be generated by pattern learning when output values yi is provided for (n) input values xi (i=1, 2, . . . , n). In pattern learning, the distance between input values resulting in different output values (y) is called a margin.
Knowledge information obtained by pattern learning means 213 is a threshold required to detect and classify the fault or is, in a feature space with a combination of abnormality levels, a hyperplane required to classify the fault as one of a plurality of classes.
If the fault determination result presented to the maintenance operator by determination result display section 216 indicates that the apparent fault is, in practice, not a fault, determination correction input section 217 is used to input corresponding information to case storage section 212.
Owing to such an operation, unlike fault analysis apparatus 100 shown in FIG. 1, fault analysis apparatus 200 shown in FIG. 2 can detect faults without the need to set a threshold for fault detection and classification.
However, disadvantageously, the above-described fault analysis apparatuses cannot detect or classify or cannot accurately detect such a fault as may produce an adverse effect not indicated by a variable indicating the abnormality level but by a variable other than the one indicating the abnormality level, for example, a variable for the number of data transmissions during a predetermined period of inter-apparatus communication, even if the maintenance operator registers relevant fault cases.